The present disclosure relates generally to image-based classification systems. The present disclosure relates more particularly to occlusion handling in image-based classification systems.
In image-based classification systems, such as a convolutional neural network classification system or an artificial neural network system, an object of interest is often partially occluded by some foreign objects. Partial occlusions can introduce noise into the classification process and often results in misclassification of the object of interest. To remedy the misclassification problem created by partial occlusions, some image-based classification systems perform a preliminary step of detecting foreign objects and removing them from the original image before proceeding with the classification process. The added step of identifying and removing foreign objects is computationally expensive and inaccurate. Another solution used in convolutional neural network image-based classification systems involves including various combinations of occlusions in the training dataset through either manually labeling or automatic augmentation. A training dataset including such combinations of occlusions is difficult to construct and not scalable when there are multiple types of foreign objects that might occlude the image.